IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-032-19012-3_6.html

Hybrid Spatio-Temporal NSGA-II–TOPSIS Optimization Framework for Intelligent Urban Water Network Management

Author

Listed:
  • J. Vijitha Ananthi

    (Vignan’s Foundation for Science, Technology & Research (Deemed to Be University), Department of CSE, School of Computing and Informatics)

  • James Deva Koresh Hezekiah

    (Vignan’s Foundation for Science, Technology & Research (Deemed to Be University), Department of CSE, School of Computing and Informatics)

Abstract

In interconnected urban water systems, the major issue raised during the planning and management phase such as rapid urbanization, climate variability, and growing environmental pressures. To overcome this research issues, proposed the Hybrid Adaptive Spatio-Temporal Evolutionary Multi-Objective Optimization (HAST-EMO) framework for robust urban water network management. This proposed approach integrates the spatio temporal method, NSGA-II-based optimization, and TOPSIS decision support later for the better urban water network management. The existing multi-objective optimization approaches struggles to handle the various dynamic water infrastructure environment. But the proposed framework provides an efficient deployment and time dependent framework for the water management systems. Simulation results show that the proposed framework provides better performance in pressure violation (60%), energy consumption (18%), faster convergence (38%), and flood volume (31%).

Suggested Citation

  • J. Vijitha Ananthi & James Deva Koresh Hezekiah, 2026. "Hybrid Spatio-Temporal NSGA-II–TOPSIS Optimization Framework for Intelligent Urban Water Network Management," Springer Optimization and Its Applications,, Springer.
    • Handle: RePEc:spr:spochp:978-3-032-19012-3_6
    DOI: 10.1007/978-3-032-19012-3_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-3-032-19012-3_6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.